Skip to content
aiqbench takeMachine Learning

aiqbench analysis

This research demonstrates the application of advanced ontology alignment techniques—specifically using OWL reasoning and HermiT—to unify disparate UK and NATO defence data standards (IES, HQDM, BFO). By establishing a rigorous crosswalk between these critical infrastructure models, the work advances the Machine Learning field's ability to handle complex, high-stakes semantic interoperability in large-scale systems.
Sources coveringarXiv, Latent Space
What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

Consistency Is Not Coherence: Orientation Search for Certified Alignments Between 4D Defence Upper Ontologies

arXiv1 min read

arXiv:2608.21914v1 Announce Type: new Abstract: We align three upper ontologies that sit under UK and NATO defence data infrastructure: the Information Exchange Standard (IES), the Higher Quality Data Model (HQDM) that underpins the National Digital Twin, and Basic Formal Ontology (BFO). No public alignment between IES and HQDM existed. Promoting a hand-curated 17-correspondence crosswalk to OWL and reasoning over the complete merged ontologies with HermiT produces three results that we believe matter beyond this pair.

Abstract:We align three upper ontologies that sit under UK and NATO defence data infrastructure: the Information Exchange Standard (IES), the Higher Quality Data Model (HQDM) that underpins the National Digital Twin, and Basic Formal Ontology (BFO). No public alignment between IES and HQDM existed. Promoting a hand-curated 17-correspondence crosswalk to OWL and reasoning over the complete merged ontologies with HermiT produces three results that we believe matter beyond this pair.

14 pages. Long paper accepted at the Ontology Matching 2026 workshop (OM 2026), co-located with ISWC 2026. Code and data: this https URL

Artificial Intelligence (cs.AI); Databases (cs.DB); Logic in Computer Science (cs.LO)

arXiv:2608.21914 [cs.AI]

(or arXiv:2608.21914v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.21914 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Full text under the publisher's open licence · 194 words

Related coverage